AI /ML Engineer

Data Pipeline Development:

Design, build and maintain scalable, efficient, and reliable data pipelines to ingest, transform, and store large volumes of structured and unstructured data.

Collaborate with cross-functional teams to gather requirements, understand data sources and implement robust ETL (Extract, Transform, Load) processes.

Machine Learning Model Development:

Develop and deploy machine learning models for various applications such as predictive analytics, recommendation systems, and anomaly detection.

Utilize techniques in statistical analysis, data mining, and machine learning to extract insights and solve complex business problems.

Data Processing and Analysis:

Conduct exploratory data analysis to discover trends, patterns, and relationships in the data.

Perform data cleaning, feature engineering, and statistical analysis to prepare data for modeling.

Model Evaluation and Optimization:

Evaluate model performance using metrics such as accuracy, precision, recall, and AUC-ROC.

Implement model tuning and optimization techniques to improve performance and efficiency.

Collaboration and Communication :

Collaborate closely with data scientists, software engineers, and business stakeholders to understand requirements and deliver solutions.

Communicate findings and insights effectively to both technical and non-technical audiences through reports, presentations, and dashboards.

Infrastructure and Tools:

Work with cloud-based platforms such as AWS, Azure, or Google Cloud for data storage, processing, and deployment.

Utilize tools and frameworks such as Apache Spark, TensorFlow, PyTorch, and sci-kit-learn for model development and deployment.

Continuous Learning and Innovation:

Stay updated with the latest trends and advancements in data science, machine learning, and big data technologies.

Propose and implement innovative solutions to enhance data-driven decision-making and business processes.

Qualifications:

Bachelor s or Master s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field.

Proven experience as a Data Engineer, Machine Learning Engineer, or similar role.

Strong proficiency in programming languages such as Python, Java, Scala, or R.

Experience with big data technologies such as Hadoop, Spark, or Kafka.

Knowledge of database systems (SQL, NoSQL) and data warehousing concepts.

Strong experience with machine learning techniques and libraries (e.g., TensorFlow, scikit-learn, PyTorch).

Experience working with cloud platforms and services (AWS, Azure, Google Cloud).

Excellent problem-solving skills and the ability to work in a fast-paced, collaborative environment.

Strong communication skills with the ability to explain complex technical concepts to non-technical stakeholders.